基于多规则的船体分段建造空间调度方法

为了解决船体分段在矩形平台建造计划的时空冲突问题,提出了基于时间和空间相似性的分段分类规则。根据分类规则对同一计划期内待调度分段进行归类,形成若干分段类并得到调度计划时间。按照最优可用点搜索法将分段类进行工作平台上的布局安排。对同类分段按照调度计划确定其建造位置和顺序。最后利用软件仿真实际船舶分段建造的空间调度过程,表明该方法是可行且实用的。     

A three-stage decomposition approach for energy-aware scheduling with processing-time-dependent product quality

Due to increasing concerns about energy and environmental demands, decision-makers in industrial companies have developed awareness about energy use and energy efficiency when engaging in short-term production scheduling and planning. This paper studied a flow-shop scheduling problem consisting of a series of processing stages and one final quality check stage with the aim of minimising energy consumption. In particular, the product quality in the problem depends on its processing time at each stage, and the energy consumption is related to the processing speed, equipment state and product quality. A novel three-stage decomposition approach is presented to solve the proposed energy-aware scheduling (EAS) problem. The decomposition approach can drastically reduce the search space and provide reliable solutions for the EAS problem. The numerical experiments show that the computational results can achieve an optimality gap of less than 4% when compared to the global optimal solutions. The parameter analysis demonstrates the managerial implications of the proposed problem. For example, increasing the number of alternative processing speeds or relaxing the delivery date will increase energy efficiency. The energy-saving potential is illustrated by comparing the scheduling results using the proposed approach and human experience.     

A decomposition-based approach to flexible flow shop scheduling under machine breakdown

Manufacturing systems in real-world production are generally dynamic and often subject to a wide range of uncertainties. Recently, research on production scheduling under uncertainty has attracted substantial attention. Although some methods have been developed to address this problem, scheduling under uncertainty remains inherently difficult to solve by any single approach. This article considers makespan optimisation of a flexible flow shop (FFS) scheduling problem under machine breakdown. It proposes a novel decomposition-based approach to decompose an FFS scheduling problem into several cluster scheduling problems which can be solved more easily by different approaches. A neighbouring K-means clustering algorithm is developed to first group the machines of an FFS into an appropriate number of machine clusters, based on a proposed machine allocation algorithm and weighted cluster validity indices. Two optimal back propagation networks, corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each machine cluster to solve cluster scheduling problems. If two neighbouring machine clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling under machine breakdown.     

An improved robust buffer allocation method for the project scheduling problem

Unpredictable uncertainties cause delays and additional costs for projects. Often, when using traditional approaches, the optimizing procedure of the baseline project plan fails and leads to delays. In this study, a two-stage multi-objective buffer allocation approach is applied for robust project scheduling. In the first stage, some decisions are made on buffer sizes and allocation to the project activities. A set of Pareto-optimal robust schedules is designed using the meta-heuristic non-dominated sorting genetic algorithm (NSGA-II) based on the decisions made in the buffer allocation step. In the second stage, the Pareto solutions are evaluated in terms of the deviation from the initial start time and due dates. The proposed approach was implemented on a real dam construction project. The outcomes indicated that the obtained buffered schedule reduces the cost of disruptions by 17.7% compared with the baseline plan, with an increase of about 0.3% in the project completion time.     

Scenario-based multi-objective robust scheduling for a semiconductor production line

Scheduling problems of semiconductor manufacturing systems (SMS) with the goal of optimising some classical performance indices (NP-hard), tend to be increasingly complicated due to stochastic uncertainties. This paper targets the robust scheduling problem of an SMS with uncertain processing times. A three-stage multi-objective robust optimisation (MORO) approach is proposed, that can collaboratively optimise the performance indices and their robustness measures. In the first stage, this paper studies the scheduling problem in the deterministic environment and obtains feasible scheduling strategies that perform well in four performance indices (the average cycle time (CT), the on-time delivery rate (ODR), the throughput (TP), and the total movement amount of wafers (MOV)). Then, in the second stage, the uncertainties are introduced into the production system. In the third stage, this paper proposes a hybrid method consisting of scenario planning, discrete simulation, and multi-objective optimisation to obtain an approximately and more robust optimal solution from the feasible scheduling strategy set. The proposed MORO approach is tested in a semiconductor experiment production line and makes a full analysis to illustrate the effectiveness of our method. The results show that our MORO is superior concerning the total robustness with multi-objective.     

Ant colony optimisation with random selection for block transportation scheduling with heterogeneous transporters in a shipyard

This paper considers a scheduling problem of heterogeneous transporters for pickup and delivery blocks in a shipyard assuming a static environment where all transportation requirements for blocks are predetermined. In the block transportation scheduling problem, the important issue is to determine which transporter delivers the block from one plant to the other plant and when, in order to minimise total logistic times. Therefore, the objective of the problem is to simultaneously determine the allocation policy of blocks and the sequence policy of transporters to minimise the weighted sum of empty transporter travel times, delay times, and tardy times. A mathematical model for the optimal solution is derived and an ant colony optimisation algorithm with random selection (ACO_RS) is proposed. To demonstrate the performance of ACO_RS, computational experiments are implemented in comparing the solution with the optimal solutions obtained by CPLEX in small-sized problems and the solutions obtained by conventional ACO in large-sized problems.     

Synchronization of yard truck scheduling and storage allocation in container terminals

Yard truck scheduling and storage allocation, as two separate subproblems in port operations, have been extensively studied in the past decades. However, from the operational point of view, they are highly interdependent. This article proposes an integer programming model in which yard truck scheduling and storage allocation problems are formulated as a whole for heterogeneous import containers. Different stacking times at yard blocks is modelled as well. The objective of the proposed model is to reduce the congestion and waiting time of yard trucks in the terminal so as to decrease the makespan of discharging containers. Owing to the inherent computational complexity, a genetic algorithm and a greedy heuristic algorithm have been designed. Computational experiments show that the proposed genetic algorithm and greedy algorithm are both effective in solving the studied problem.     

A Three Phased Approach To Final Exam Scheduling

A multi-phase examination scheduling process applicable to large university settings in general and SUNY at Buffalo (SUNYAB) in particular is proposed. Each scheduling phase is considered an integral part of the overall scheduling process and solved independently. Phase one of scheduling process is wth the assignment of examinations to exam blocks (each containing one or more exams). The objective of this phase is to minimize the number of students taking more than one exam in the same exam block. The problem is solved using a variation of the quadratic assignment problem. Phase two of the scheduling process uses the results from phase one as input. The exam blocks are assigned to exam days in such a way that some measure of students' comfort is maintained. Phase two of the scheduling process is formulated as a set covering problem with an extra constraint. Phase three of the scheduling process which is involved wt h the assignment of exam blocks to exam periods in each day and optimal ordering of exam days is solved heuristically using a traveling salesman problem as part of solution procedure. The performance of the algorithms devised for the multi-phase scheduling process are tested both in terms of quality of the solutions obtained and the computer time to generate these solutions.     

A multi-objective scatter search for the ladle scheduling problem

We consider the ladle scheduling problem, which can be regarded as a vehicle routing problem with semi-soft time windows and adjustment times. The problem concerns allocating ladles to serve molten steel based on a given steelmaking scheduling plan, and determining the modification operations for the empty ladles after the service process. In addition, combining the controllable processing time of molten steel, the other aspect of the problem is to determine the service start times taking into consideration the technological constraints imposed in practice. We present a non-linear mathematical programming model with the conflicting objectives of minimising the occupation ratio of the ladles and maximising the degree of satisfaction with meeting the soft windows. To solve the multi-objective model, we develop a new scatter search (SS) approach by re-designing the common components of SS and incorporating a diversification generator, a combination method and a diversification criterion to conduct a wide exploration of the search space. We analyse and compare the performance of the proposed approach with a multi-objective genetic algorithm and with manual scheduling adopted in practical production using three real-life instances from a well-known iron–steel production plant in China. The computational results demonstrate the effectiveness of the proposed SS approach for solving the ladle scheduling problem.     

面向定制生产系统的缓冲区优化配置方法

针对定制型装备制造企业中具有有限缓冲区的开排队网制造单元,其车间负荷界限即缓冲设置难以确定的问题（buffer allocation problem,BAP）,文章对每阶段具有有限缓冲区且含有多台加工设备的三阶段柔性流水车间(flexible flow shop,FFS)进行排队网建模,应用状态空间分解法对该模型进行分析求解,获得系统的一系列性能指标值。为了对该方法的有效性进行验证,对该模型设计仿真实验,并利用扩展法对模型进行求解,将数值结果进行比较分析,验证了利用该方法对FFS缓冲区进行优化配置的合理性,这对较大规模的多节点每阶段具有多台设备的流水车间负荷界限的有效设定及其规划具有参考和指导意义。     

基于优先规则的船舶分段空间调度算法

为有效解决船舶分段的空间调度问题,提出了一种基于优先规则的求解算法。首先利用优先规则和禁忌搜索算法产生可行的分段调度序列,再采用一种启发式定位策略——最下最左填满策略对产生的调度序列进行解码,以评估调度序列的优劣。算法不断迭代,最终可得到近似最优解。对船厂的实际生产数据进行了实证分析,并与现有的算法进行了对比,验证了所提出的算法在空间调度问题上的有效性和优越性。     

Emission-constrained optimal generation scheduling incorporating equity principles

An annual emission-constrained generation scheduling model that combines equity principles with cost optimisation is developed. By employing this annual generation scheduling model, an estimation of the daily emission allowance of each generation unit is obtained. A daily emission-constrained generation scheduling model, based on cost optimisation, is proposed. The equity-related issues in the daily scheduling model are discussed. The Shapley value is employed in the daily scheduling problem to allocate the operating cost reduction among the units. Characteristics of the allocation game of operating cost reduction in daily scheduling are discovered, and several methods are proposed to overcome the combinatorial explosion problem in the calculation of the Shapley value. The effectiveness of the proposed models is shown with simulation results on a test power system.     

An energy-efficient scheduling and rescheduling method for production and logistics systems†

Scheduling can be defined as the allocation of available resources over time while optimising a set of criteria like early completion time of task, holding inventory, etc. The complexity of the scheduling problem, already known to be high, increases if dynamic events and disruptions are considered. In addition, in production and logistics, designers of scheduling systems must consider sustainability-related expectations. This paper presents an energy-efficient scheduling and rescheduling method (named Green Rescheduling Method, GRM). GRM aims at the solving of the dynamic scheduling problem under the condition of a certain level of routing flexibility enabling the reassignment of tasks to new resources. The key performance indicators integrated into the proposed GRM are effectiveness and efficiency-oriented. Applications concern the domains of production and logistics. In order to assess the proposed approach, experimentations have been made and results illustrate the applicability of GRM to build efficient and effective scheduling and rescheduling both for flexible manufacturing systems and inventory distribution systems in a physical internet network. A mathematical formulation for flexible job shop problem with energy consumption is also proposed using mixed Integer programming to evaluate the performance of the predictive part of GRM.     

A meta-heuristic approach for solving the no-wait flow-shop problem

No-wait flow-shop scheduling problems refer to the set of problems in which a number of jobs are available for processing on a number of machines in a flow-shop context with the added constraint that there should be no waiting time between consecutive operations of the jobs. The problem is strongly NP-hard. In this paper, the considered performance measure is the makespan. In order to explore the feasible region of the problem, a hybrid algorithm of Tabu Search and Particle Swarm Optimisation (PSO) is proposed. In the proposed approach, PSO algorithm is used in order to move from one solution to a neighbourhood solution. We first employ a new coding and decoding technique to efficiently map the discrete feasible space to the set of integer numbers. The proposed PSO will further use this coding technique to explore the solution space and move from one solution to a neighbourhood solution. Afterwards, the algorithm decodes the solutions to its respective feasible solution in the discrete feasible space and returns the new solutions to the TS. The algorithm is tested by solving a large number of problems available in the literature. Computational results show that the proposed algorithm is able to outperform competitive methods and improves some of the best-known solutions of the considered test problems.     

Production scheduling optimization algorithm for the hot rolling processes

The hot rolling production scheduling problem is an extremely difficult and time-consuming process, so it is quite difficult to achieve an optimal solution with traditional optimization methods owing to the high computational complexity. To ensure the feasibility of solutions and improve the efficiency of the scheduling, this paper proposes a vehicle routing problem (VRP) to model the problem and develops an easily implemented hybrid approach (QPSO-SA) to solve the problem. In the hybrid approach, quantum particle swarm optimization (QPSO) combines local search and global search to search the optimal results and simulated annealing (SA) employs certain probability to avoid getting into a local optimum. The computational results from actual production data have shown that the proposed model and algorithm are feasible and effective for the hot rolling scheduling problem.     

MIP approach to balancing transfer lines with blocks of parallel operations

A novel line balancing problem is considered. It differs from assembly line balancing problems in that the operations of each workstation are partitioned into blocks of simultaneously executed (parallel) operations. The blocks of each workstation are executed sequentially. For the line design stage considered in this paper, the compatibility (inclusion and exclusion) constraints for grouping operations into blocks and workstations as well as precedence constraints are known. The goal is to minimize a weighted sum of the number of workstations and the number of blocks while achieving a desired cycle time and satisfying all the constraints. The developed exact and heuristic methods are based on a mixed-integer programming approach. Experimental results are reported.     

Dynamic allocation of surplus by-product gas in a steel plant by dynamic programming with a reduced state space algorithm

Surplus by-product gas (SBPG) in a steel plant is the difference between gas production and consumption. Dynamic programming (DP) has been observed to be a useful method for SBPG dynamic allocation. However, in the SBPG allocation problem, standard dynamic programming (SDP) usually suffers from dimensionality. In this study, a novel dynamic programming method with a reduced state space algorithm (RSS-DP) is proposed. By decomposing the amount of SBPG into the reference and subsequent allocation, RSS-DP reduces the state space of the SDP model significantly such that the computation time is significantly reduced. An example of a five-boiler allocation of SBPG and a real-world online allocation of SBPG in these five boilers of a steel plant are implemented to exhibit the effectiveness of the proposed algorithm. In both cases, the solutions obtained using the proposed method are better than those obtained by traditional methods, in both computation time and energy benefit.     

Block spatial scheduling modelling and application in shipbuilding

With the wide application of module-shipbuilding technology, problems related to block spatial scheduling occur in various working areas, and this restricts the productivity of shipbuilding. To address the problems and to obtain the optimum block sequence and spatial layout, typical block features and work plates were investigated. A heuristic spatial scheduling model was established based on the investigation and proposed strategies with the objective to minimise makespan. With the heuristic algorithm, a block spatial scheduling system was developed and implemented with real data from a large ship. Through the spatial scheduling system, visual results of daily block layouts and progress charts for all blocks can be easily obtained and work orders can also be created for site workers. Several other spatial scheduling methods are described and compared with the above-mentioned heuristic algorithm. The result shows that the heuristic algorithm is better than Cplex and a genetic algorithm in solving large-scale block scheduling, and the heuristic algorithm is better than a grid algorithm and manual scheduling in all aspects such as makespan, utilisation of work plates, runtime of scheduling and on-time delivery. The developed block spatial scheduling system is applied in a block production shop of a modern shipyard and shows good performance.     

Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Cloud computing is currently dominated within the space of high-performance distributed computing and it provides resource polling and on-demand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.